{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from internal.dataparsers.colmap_dataparser import Colmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = os.path.expanduser(\"~/dataset/dbl/parsed_from_exif-sift_matching/dense_1600-from_hie_map\")\n",
    "partition_path = os.path.join(dataset_path, \"partitions-size_5.0-enlarge_0.1-visibility_0.9_0.1\")\n",
    "partition_id_str = \"000_003\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "appearance group by camera id\n",
      "loading colmap 3D points\n",
      "[WARNING]Some images can not be found in colmap sparse model: ['h/22h00395.JPG', 'h/22h00455.JPG', 'h/22h00515.JPG', 'h/22h00576.JPG', 'h/22h00636.JPG', 'h/22h00696.JPG', 'h/22h00756.JPG', 'h/22h00816.JPG', 'h/22h00876.JPG', 'h/22h00949.JPG', 'h/22h01009.JPG', 'h/22h01069.JPG', 'h/22h01189.JPG', 'h/22h01259.JPG', 'h/22h01319.JPG', 'h/22h01380.JPG', 'h/22h01450.JPG', 'h/22h01510.JPG', 'h/22h01574.JPG', 'h/22h01634.JPG', 'h/22h01694.JPG', 'h/22h01754.JPG', 'h/23h00116.JPG', 'h/23h00176.JPG', 'h/23h00236.JPG', 'h/24h00024.JPG', 'h/24h00084.JPG', 'h/24h00144.JPG', 'q/22q00342.JPG', 'q/22q00402.JPG', 'q/22q00462.JPG', 'q/22q00522.JPG', 'q/22q00582.JPG', 'q/22q00642.JPG', 'q/22q00703.JPG', 'q/22q00763.JPG', 'q/22q00824.JPG', 'q/22q00887.JPG', 'q/22q00953.JPG', 'q/22q01013.JPG', 'q/22q01075.JPG', 'q/22q01135.JPG', 'q/22q01196.JPG', 'q/22q01265.JPG', 'q/22q01325.JPG', 'q/22q01394.JPG', 'q/22q01514.JPG', 'q/22q01576.JPG', 'q/22q01696.JPG', 'q/22q01756.JPG', 'q/23q00058.JPG', 'q/23q00118.JPG', 'q/23q00178.JPG', 'q/23q00238.JPG', 'q/24q00027.JPG', 'q/24q00089.JPG', 'q/24q00149.JPG', 'x/22x00349.JPG', 'x/22x00409.JPG', 'x/22x00469.JPG', 'x/22x00529.JPG', 'x/22x00589.JPG', 'x/22x00649.JPG', 'x/22x00709.JPG', 'x/22x00769.JPG', 'x/22x00829.JPG', 'x/22x00891.JPG', 'x/22x00951.JPG', 'x/22x01011.JPG', 'x/22x01071.JPG', 'x/22x01131.JPG', 'x/22x01191.JPG', 'x/22x01253.JPG', 'x/22x01313.JPG', 'x/22x01373.JPG', 'x/22x01435.JPG', 'x/22x01495.JPG', 'x/22x01555.JPG', 'x/22x01675.JPG', 'x/22x01735.JPG', 'x/23x00037.JPG', 'x/23x00097.JPG', 'x/23x00157.JPG', 'x/23x00217.JPG', 'x/24x00005.JPG', 'x/24x00065.JPG', 'x/24x00125.JPG', 'x/24x00185.JPG', 'y/22y00383.JPG', 'y/22y00443.JPG', 'y/22y00503.JPG', 'y/22y00564.JPG', 'y/22y00625.JPG', 'y/22y00685.JPG', 'y/22y00745.JPG', 'y/22y00805.JPG', 'y/22y00865.JPG', 'y/22y00948.JPG', 'y/22y01008.JPG', 'y/22y01068.JPG', 'y/22y01128.JPG', 'y/22y01188.JPG', 'y/22y01248.JPG', 'y/22y01308.JPG', 'y/22y01368.JPG', 'y/22y01441.JPG', 'y/22y01501.JPG', 'y/22y01566.JPG', 'y/22y01626.JPG', 'y/22y01686.JPG', 'y/22y01747.JPG', 'y/23y00109.JPG', 'y/23y00169.JPG', 'y/23y00229.JPG', 'y/24y00017.JPG', 'y/24y00077.JPG', 'y/24y00138.JPG', 'z/22z00337.JPG', 'z/22z00397.JPG', 'z/22z00458.JPG', 'z/22z00518.JPG', 'z/22z00578.JPG', 'z/22z00638.JPG', 'z/22z00698.JPG', 'z/22z00758.JPG', 'z/22z00818.JPG', 'z/22z00878.JPG', 'z/22z00945.JPG', 'z/22z01005.JPG', 'z/22z01065.JPG', 'z/22z01185.JPG', 'z/22z01263.JPG', 'z/22z01383.JPG', 'z/22z01449.JPG', 'z/22z01509.JPG', 'z/22z01569.JPG', 'z/22z01629.JPG', 'z/22z01689.JPG', 'z/22z01749.JPG', 'z/23z00111.JPG', 'z/23z00231.JPG', 'z/24z00079.JPG']\n",
      "[colmap dataparser] train set images: 678, val set images: 11, loaded mask: 0\n"
     ]
    }
   ],
   "source": [
    "dataparser_outputs = Colmap(\n",
    "    image_list=os.path.join(partition_path, \"{}.txt\".format(partition_id_str)),\n",
    "    eval_image_select_mode=\"list-optional\",\n",
    "    eval_list=os.path.join(dataset_path, \"test_set.txt\"),\n",
    "    split_mode=\"experiment\",\n",
    ").instantiate(dataset_path, 0, 0).get_outputs()\n",
    "# dataparser_outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load partition data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from internal.utils.partitioning_utils import PartitionCoordinates\n",
    "partition_data = torch.load(os.path.join(partition_path, \"partitions.pt\"))\n",
    "partition_coordinates = PartitionCoordinates(**partition_data[\"partition_coordinates\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition_idx = -1\n",
    "for idx, _ in enumerate(partition_coordinates):\n",
    "    if partition_coordinates.get_str_id(idx) == partition_id_str:\n",
    "        partition_idx = idx\n",
    "        break\n",
    "assert partition_idx >= 0\n",
    "partition_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition_data[\"scene_config\"][\"partition_size\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0., 19.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# partition center\n",
    "partition_box = partition_coordinates.get_bounding_boxes(partition_data[\"scene_config\"][\"partition_size\"])[partition_idx]\n",
    "partition_center = (partition_box.min + partition_box.max) * 0.5\n",
    "partition_center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create model and density controller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from internal.models.vanilla_gaussian import VanillaGaussian\n",
    "gaussian_model = VanillaGaussian(\n",
    "    sh_degree=0,\n",
    ").instantiate()\n",
    "gaussian_model.setup_from_pcd(xyz=dataparser_outputs.point_cloud.xyz, rgb=dataparser_outputs.point_cloud.rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "partition_idx=#46, id=tensor([0, 3], dtype=torch.int32), transform=[[0.9906935691833496, -0.1124858558177948, 0.07663705945014954, 0.0], [-0.1015680804848671, -0.9857741594314575, -0.13391463458538055, 0.0], [0.0906103253364563, 0.1248844787478447, -0.9880251288414001, 0.0], [0.0, 0.0, 0.0, 1.0]], center=[0.0, 19.0], radius=3.535533905029297\n"
     ]
    }
   ],
   "source": [
    "from internal.density_controllers.foreground_first_density_controller import ForegroundFirstDensityController\n",
    "density_controller = ForegroundFirstDensityController(\n",
    "    partition=partition_path,\n",
    "    partition_idx=partition_idx,\n",
    ").instantiate()\n",
    "density_controller.setup(stage=\"validate\", pl_module=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.9907, -0.1125,  0.0766,  0.0000],\n",
       "        [-0.1016, -0.9858, -0.1339,  0.0000],\n",
       "        [ 0.0906,  0.1249, -0.9880,  0.0000],\n",
       "        [ 0.0000,  0.0000,  0.0000,  1.0000]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "density_controller.rotation_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_partition_gaussian_mask(\n",
    "        means: torch.Tensor,\n",
    "        partition_bounding_box,\n",
    "        orientation_transform: torch.Tensor = None,\n",
    "):\n",
    "    if orientation_transform is not None:\n",
    "        means = means @ orientation_transform[:3, :3].T\n",
    "\n",
    "    # include min bound, exclude max bound\n",
    "    is_ge_min = torch.prod(torch.ge(means[..., :2], partition_bounding_box.min), dim=-1)\n",
    "    is_lt_max = torch.prod(torch.lt(means[..., :2], partition_bounding_box.max), dim=-1)\n",
    "    is_in_bounding_box = torch.logical_and(is_ge_min, is_lt_max)\n",
    "\n",
    "    return is_in_bounding_box, means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_inside_partition, transformed_means = get_partition_gaussian_mask(\n",
    "    gaussian_model.get_means(),\n",
    "    partition_bounding_box=partition_box,\n",
    "    orientation_transform=partition_data[\"extra_data\"][\"rotation_transform\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get grad factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_factors = density_controller._get_grad_decay_factors(gaussian_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert torch.allclose(density_controller.partition_center, partition_center), (density_controller.partition_center, partition_center)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert torch.allclose(grad_factors[is_inside_partition], torch.tensor(1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.5355)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition_radius = density_controller.partition_radius\n",
    "partition_radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = torch.norm(transformed_means[:, :2] - partition_center, dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#0, distance=5.698302268981934, distance_radius_factor=1.6117233037948608, grad_factor=2.0\n",
      "#16, distance=4.107719421386719, distance_radius_factor=1.1618385314941406, grad_factor=1.3236770629882812\n",
      "#32, distance=3.1625254154205322, distance_radius_factor=0.894497275352478, grad_factor=1.0\n"
     ]
    }
   ],
   "source": [
    "for idx in [0, 16, 32]:\n",
    "    print(\"#{}, distance={}, distance_radius_factor={}, grad_factor={}\".format(\n",
    "        idx,\n",
    "        distances[idx],\n",
    "        distances[idx] / partition_radius,\n",
    "        grad_factors[idx],\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxBoundingBox(min=tensor([-2.5000, 16.5000]), max=tensor([ 2.5000, 21.5000]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "with torch.no_grad():\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    ax.set_xlim([\n",
    "        transformed_means[:, 0].min(),\n",
    "        transformed_means[:, 0].max(),\n",
    "    ])\n",
    "    ax.set_ylim([\n",
    "        transformed_means[:, 1].min(),\n",
    "        transformed_means[:, 1].max(),\n",
    "    ])\n",
    "    ax.scatter(\n",
    "        x=transformed_means[::16, 0],\n",
    "        y=transformed_means[::16, 1],\n",
    "        s=0.01,\n",
    "        c=grad_factors[::16] - 1.\n",
    "    )\n",
    "    # all of the rectangles and circles share a common center\n",
    "    ax.add_artist(mpatches.Rectangle(\n",
    "        partition_box.min.tolist(),\n",
    "        partition_box.max[0] - partition_box.min[0],\n",
    "        partition_box.max[1] - partition_box.min[1],\n",
    "        fill=False,\n",
    "        color=\"green\",\n",
    "    ))\n",
    "    # grad_factor=1 inside this circle\n",
    "    ax.add_artist(mpatches.Circle(\n",
    "        xy=partition_center.tolist(),\n",
    "        radius=partition_radius,\n",
    "        fill=False,\n",
    "        color=\"red\",\n",
    "    ))\n",
    "    # grad_factor=2 outside this\n",
    "    ax.add_artist(mpatches.Circle(\n",
    "        xy=partition_center.tolist(),\n",
    "        radius=partition_radius * density_controller.config.max_radius_factor,\n",
    "        fill=False,\n",
    "        color=\"blue\",\n",
    "    ))\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
